TITLE:
Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling
AUTHORS:
Mohan Sathya Priya, Radhakrishnan Kanthavel, Muthusamy Saravanan
KEYWORDS:
Steam Boiler, Fouling and Slagging, Fuzzy Clustering, Artificial Neural Networks
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.12,
October
31,
2016
ABSTRACT: The slagging/fouling due
to the accession of fireside deposits on the steam boilers decreases boiler
efficiency and availability which leads to unexpected shut-downs. Since it is
inevitably associated with the three major factors namely the fuel
characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by
varying the above three factors. The research develops a generic slagging/fouling
prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks
(FCANN). The FCANN model presents a good accuracy of 99.85% which makes this
model fast in response and easy to be updated with lesser time when compared to
single ANN. The comparison between
predictions and observations is found to be satisfactory with less input
parameters. This should be capable of giving relatively quick responses
while being easily implemented for various furnace types.